Before
Our client has a multitude of disparate data sources requiring integration. For each individual table within a data source, a distinct package was manually made to facilitate the processing and subsequent loading into an on-premises SQL Server database. This procedure necessitated extensive manual transformations, which consumed considerable time and resources. Consequently, the output was rigid, offering minimal scope for customization. Moreover, the manual aspect of these operations often contained errors. With the escalation of data volumes, SQL Server was an expensive and underperforming element, presenting challenges in terms of management and scalability.

The journey
Upon meticulous evaluation and assessment of the current infrastructure, we pinpointed the principal bottleneck. In response, we introduced our metadata-driven framework designed to easily streamline the assimilation of data from diverse sources into a centralized cloud-based data lake. Our framework’s reliance on metadata, coupled with its foundation on established infrastructure, enabled us to expedite the data ingestion process. Subsequently, leveraging the comprehensively historized data, we proceeded to execute the business logic.